the anonymous

Design , Architect , Develop and FAIL !...

Full-time Reposts, part-time replies.

[Hareesh Vemasani @HVemasani]

If you want to start a startup:

Claude = coding. ($20/mo)
Supabase = backend. (Free)
Vercel = deploying. (Free)
Namecheap = domain. ($12/yr)
Stripe = payments. (2.9%/transaction)
GitHub = version control. (Free)
Resend = emails. (Free)
ProductBridge = feedback (Free)
Clerk = auth. (Free)
Cloudflare = DNS. (Free)
PostHog = analytics. (Free)
Sentry = error tracking. (Free)
Upstash = Redis. (Free)
Pinecone = vector DB. (Free)

Total monthly cost to run a startup: ~$20

[Anatoli Kopadze @AnatoliKopadze]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

In 45 minutes she shows exactly how Anthropic builds agents that remember, fix their own mistakes and get smarter with every run.

This beats any paid course on agents I've seen.

Watch it, then read the guide on building loops below.

[Arpit Bhayani @arpit_bhayani]

I missed reading the newspaper, so I built one to keep me caught up with all things AI and engineering :)

Every day there is more AI, database, and engineering news than anyone can keep up with. I used to follow a series of steps, and now, I have turned it into a simple website https://t.co/IyLtJNHLWy - It holds the most interesting stories across AI, databases, and engineering in one place.

Every story is pulled from arXiv and Hacker News, then ranked based on how interesting and useful I find it. It is slightly tuned to my taste, so if you enjoy what I usually post, you will enjoy this curation as well.

By the way, you can also filter by source (arXiv or Hacker News) and signal level (a combination of interestingness and helpfulness). On busy days, you can jump straight to stories marked Notable, Recommended, Must-Read, or Essential.

I want this to be the tab that stays open on one monitor. Your agents run their tasks on one side, and you catch up on what matters on the other :)

No feed to scroll. Just stuff worth knowing.

Hope this helps.

[Suraj Sharma @suraj_sharma14]

If you can build these 12 Agentic AI projects.

You're hired.

Project 1: Structured Output Agent
Enforce Pydantic JSON schemas, validate tool responses, retry on parse errors, log validation failures.
→ Shows: You can make LLMs reliable not random

Project 2: RAG Agent with Citation Grounding
Retrieve context, generate answers with sources, flag low-confidence responses, fallback to search.
→ Shows: You can prevent hallucinations at scale

Project 3: ReAct Planning Agent
Observe → think → act → reflect loop, max iteration limits, self-critique, graceful degradation.
→ Shows: You can build agents that don't infinite loop

Project 4: Multi-Tool Orchestrator Agent
Dynamic tool registry, capability-based routing, permission scoping, parallel execution, conflict resolution.
→ Shows: You can coordinate complex workflows

Project 5: Memory-Enabled Conversational Agent
Short-term buffer + long-term vector recall, context compression, relevance scoring, cross-session sync.
→ Shows: You can build agents that remember users

Project 6: Human-in-the-Loop Approval Agent
Uncertainty detection → pause → request human input → resume with validated context, full audit trail.
→ Shows: You can build safe, compliant systems

Project 7: Cost-Aware Agent Router
Token budgeting per task, model routing by complexity/cost, early exit on confidence, cost-per-decision analytics.
→ Shows: You can reduce infra costs by 40-60%

Project 8: Event-Triggered Automation Agent
Listen to webhooks/queues, execute workflows on triggers, idempotent execution, dead-letter handling, retry logic.
→ Shows: You can build production automation not demos

Project 9: Multi-Agent Debate System
Multiple agents propose solutions, critic evaluates, voting/consensus logic, aggregator synthesizes with confidence.
→ Shows: You can orchestrate swarms not single agents

Project 10: Self-Reflective Agent with Auto-Eval
Execute → evaluate via LLM-as-judge → critique reasoning → regenerate with constraints, log improvement metrics.
→ Shows: You can build systems that improve over time

Project 11: Production Agent with Observability
Deploy with LangSmith/Arize tracing, latency/cost dashboards, alerting on loops/failures, canary testing, rollback.
→ Shows: You can ship to production not just localhost

Project 12: Open Source Agent Framework Contribution
Extend LangGraph/CrewAI/AutoGen with new pattern, write docs + demo, publish benchmarks, submit PR + tutorial.
→ Shows: You're a community builder not just a consumer

(Bookmark this)

happy building...

[Puneet Patwari @system_monarch]

A month ago, I told everyone:

"I've been a backend Engineer for 12+ years. Today, I'm a Principal Engineer at Atlassian.

I've designed systems that handle millions of requests. Sat on both sides of system design interviews.

Reviewed more architecture docs than I can count.

Starting today, I'm breaking down the fundamentals of scaling for the next 25 days.

If you're learning system design bookmark this thread, you're going to get a lot of learning from this."

FYI, the series has concluded. Here are all the concepts, please bookmark, share, learn and the most important build from the learnings you get.

Also, if you have any ideas on what you'd like to see from me, please let me know, any other series or concepts to be broken down.

[Khairallah AL-Awady @eng_khairallah1]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

In 45 minutes she shows exactly how to build an agent that improves itself.

Most people are still doing all of this by hand.

Watch the session, then save the guide below.

[Tech with Mak @techNmak]

Harvard just leaked their Senior Engineer roadmap for free.

Stop paying for $2,000 bootcamps. Prof. Vijay Janapa Reddi just put the entire ML Systems (CS249r) curriculum on GitHub.

If you master these 6 pillars, you're ahead of 99% of the field:

🏛️ Architecture
🚿 Data Pipelines
🚢 Production
🛠️ MLOps
🔋 Edge AI
🔒 Privacy

This is the "Black Box" of Big Tech infrastructure, open-sourced.

This is the "Black Box" of Big Tech infrastructure, open-sourced.

Read. Learn. Bookmark.

[Rahul @sairahul1]

📂 AI Engineer
┃
┣ 📂 Introduction
┃ ┣ 📂 What is an AI Engineer?
┃ ┣ 📂 Roles & Responsibilities
┃ ┣ 📂 Product Development Impact
┃ ┣ 📂 AI Engineer vs ML Engineer
┃ ┗ 📂 Career Paths
┃
┣ 📂 LLM Fundamentals
┃ ┣ 📂 AI vs AGI
┃ ┣ 📂 Large Language Models (LLMs)
┃ ┣ 📂 Tokens
┃ ┣ 📂 Context Windows
┃ ┣ 📂 Training
┃ ┣ 📂 Inference
┃ ┣ 📂 Embeddings
┃ ┣ 📂 Vector Databases
┃ ┣ 📂 Fine-Tuning
┃ ┗ 📂 AI Agents
┃
┣ 📂 How LLMs Work
┃ ┣ 📂 Tokenization
┃ ┣ 📂 Attention Mechanism
┃ ┣ 📂 Sampling
┃ ┣ 📂 Temperature
┃ ┣ 📂 Top-K
┃ ┣ 📂 Top-P
┃ ┣ 📂 Repetition Penalties
┃ ┗ 📂 Generation Process
┃
┣ 📂 Prompt Engineering
┃ ┣ 📂 Zero-Shot Prompting
┃ ┣ 📂 Few-Shot Prompting
┃ ┣ 📂 Chain of Thought
┃ ┣ 📂 ReAct Prompting
┃ ┣ 📂 Function Calling
┃ ┣ 📂 System Prompting
┃ ┣ 📂 Prompt Caching
┃ ┣ 📂 Structured Outputs
┃ ┣ 📂 Prompt Templates
┃ ┗ 📂 Prompt Evaluation
┃
┣ 📂 Context Engineering
┃ ┣ 📂 External Memory
┃ ┣ 📂 Context Composition
┃ ┣ 📂 Dynamic Retrieval
┃ ┣ 📂 Context Isolation
┃ ┣ 📂 Knowledge Injection
┃ ┣ 📂 Long-Term Memory
┃ ┣ 📂 Session Memory
┃ ┗ 📂 Agent Memory Design
┃
┣ 📂 AI Models
┃ ┣ 📂 Closed Models
┃ ┃ ┣ 📂 OpenAI
┃ ┃ ┣ 📂 Anthropic Claude
┃ ┃ ┣ 📂 Google Gemini
┃ ┃ ┣ 📂 Cohere
┃ ┃ ┗ 📂 Mistral
┃ ┃
┃ ┣ 📂 Open Models
┃ ┃ ┣ 📂 Llama
┃ ┃ ┣ 📂 DeepSeek
┃ ┃ ┣ 📂 Qwen
┃ ┃ ┣ 📂 Gemma
┃ ┃ ┗ 📂 Phi
┃ ┃
┃ ┣ 📂 Hosted Models
┃ ┣ 📂 Self Hosted Models
┃ ┣ 📂 Quantization
┃ ┣ 📂 Fine-Tuning
┃ ┗ 📂 Model Evaluation
┃
┣ 📂 Model Selection
┃ ┣ 📂 Latency
┃ ┣ 📂 Cost
┃ ┣ 📂 Quality
┃ ┣ 📂 Context Length
┃ ┣ 📂 Tool Usage
┃ ┣ 📂 Reasoning Capability
┃ ┣ 📂 Multimodal Support
┃ ┗ 📂 Benchmark Comparison
┃
┣ 📂 Platforms & Ecosystem
┃ ┣ 📂 Hugging Face
┃ ┣ 📂 Ollama
┃ ┣ 📂 LM Studio
┃ ┣ 📂 OpenRouter
┃ ┣ 📂 Together AI
┃ ┣ 📂 Groq
┃ ┣ 📂 Fireworks AI
┃ ┗ 📂 Vercel AI
┃
┣ 📂 APIs & SDKs
┃ ┣ 📂 OpenAI API
┃ ┣ 📂 Claude API
┃ ┣ 📂 Gemini API
┃ ┣ 📂 OpenAI Compatible APIs
┃ ┣ 📂 Hugging Face SDK
┃ ┣ 📂 LangChain SDK
┃ ┣ 📂 LlamaIndex SDK
┃ ┗ 📂 AI Gateway Design
┃
┣ 📂 Embeddings
┃ ┣ 📂 What are Embeddings?
┃ ┣ 📂 Semantic Search
┃ ┣ 📂 Classification
┃ ┣ 📂 Recommendations
┃ ┣ 📂 Similarity Matching
┃ ┣ 📂 Clustering
┃ ┣ 📂 Anomaly Detection
┃ ┗ 📂 Embedding Evaluation
┃
┣ 📂 Embedding Models
┃ ┣ 📂 OpenAI Embeddings
┃ ┣ 📂 Gemini Embeddings
┃ ┣ 📂 Cohere Embeddings
┃ ┣ 📂 Sentence Transformers
┃ ┣ 📂 Jina Embeddings
┃ ┗ 📂 BGE Models
┃
┣ 📂 Vector Databases
┃ ┣ 📂 Pinecone
┃ ┣ 📂 Weaviate
┃ ┣ 📂 Qdrant
┃ ┣ 📂 Chroma
┃ ┣ 📂 LanceDB
┃ ┣ 📂 FAISS
┃ ┣ 📂 MongoDB Atlas
┃ ┣ 📂 Supabase
┃ ┣ 📂 Indexing
┃ ┣ 📂 Chunking
┃ ┗ 📂 Similarity Search
┃
┣ 📂 Retrieval-Augmented Generation (RAG)
┃ ┣ 📂 What is RAG?
┃ ┣ 📂 Chunking Strategies
┃ ┣ 📂 Embeddings
┃ ┣ 📂 Vector Search
┃ ┣ 📂 Retrieval
┃ ┣ 📂 Re-ranking
┃ ┣ 📂 Generation
┃ ┣ 📂 RAG vs Fine-Tuning
┃ ┣ 📂 Hybrid Search
┃ ┗ 📂 Production RAG
┃
┣ 📂 RAG Frameworks
┃ ┣ 📂 LangChain
┃ ┣ 📂 LlamaIndex
┃ ┣ 📂 Haystack
┃ ┣ 📂 RAGFlow
┃ ┣ 📂 DSPy
┃ ┗ 📂 Custom RAG Pipelines
┃
┣ 📂 AI Agents
┃ ┣ 📂 Agent Fundamentals
┃ ┣ 📂 Agent Use Cases
┃ ┣ 📂 Tool Calling
┃ ┣ 📂 ReAct
┃ ┣ 📂 Planning
┃ ┣ 📂 Reflection
┃ ┣ 📂 Memory
┃ ┣ 📂 Multi-Agent Systems
┃ ┣ 📂 Autonomous Workflows
┃ ┗ 📂 Agent Architectures
┃
┣ 📂 Agent Frameworks
┃ ┣ 📂 OpenAI Agents SDK
┃ ┣ 📂 Claude Agent SDK
┃ ┣ 📂 Google ADK
┃ ┣ 📂 Vercel AI SDK
┃ ┣ 📂 CrewAI
┃ ┣ 📂 AutoGen
┃ ┣ 📂 LangGraph
┃ ┗ 📂 Mastra
┃
┣ 📂 Model Context Protocol (MCP)
┃ ┣ 📂 MCP Host
┃ ┣ 📂 MCP Client
┃ ┣ 📂 MCP Server
┃ ┣ 📂 Transport Layer
┃ ┣ 📂 Data Layer
┃ ┣ 📂 Local MCP Servers
┃ ┣ 📂 Remote MCP Servers
┃ ┣ 📂 Building MCP Tools
┃ ┗ 📂 MCP Security
┃
┣ 📂 AI Safety
┃ ┣ 📂 Prompt Injection
┃ ┣ 📂 Jailbreak Attacks
┃ ┣ 📂 Privacy & Security
┃ ┣ 📂 Bias & Fairness
┃ ┣ 📂 Hallucinations
┃ ┣ 📂 Adversarial Testing
┃ ┣ 📂 Guardrails
┃ ┣ 📂 Output Validation
┃ ┗ 📂 Responsible AI
┃
┣ 📂 Observability
┃ ┣ 📂 Logging
┃ ┣ 📂 Tracing
┃ ┣ 📂 Cost Monitoring
┃ ┣ 📂 Latency Monitoring
┃ ┣ 📂 Production Monitoring
┃ ┣ 📂 Langfuse
┃ ┣ 📂 LangSmith
┃ ┣ 📂 Helicone
┃ ┗ 📂 Arize AI
┃
┣ 📂 Evaluations
┃ ┣ 📂 Deterministic Evals
┃ ┣ 📂 Model-Based Evals
┃ ┣ 📂 Human Evals
┃ ┣ 📂 Regression Testing
┃ ┣ 📂 Benchmarking
┃ ┣ 📂 DeepEval
┃ ┣ 📂 RAGAS
┃ ┗ 📂 Continuous Evaluation
┃
┣ 📂 Multimodal AI
┃ ┣ 📂 Image Understanding
┃ ┣ 📂 Image Generation
┃ ┣ 📂 Video Understanding
┃ ┣ 📂 Video Generation
┃ ┣ 📂 Audio Processing
┃ ┣ 📂 Speech-to-Text
┃ ┣ 📂 Text-to-Speech
┃ ┗ 📂 Vision Agents
┃
┣ 📂 Multimodal APIs
┃ ┣ 📂 OpenAI Vision
┃ ┣ 📂 Gemini Vision
┃ ┣ 📂 Whisper
┃ ┣ 📂 ElevenLabs
┃ ┣ 📂 NanoBanana
┃ ┣ 📂 Flux
┃ ┣ 📂 Veo
┃ ┗ 📂 Kling
┃
┣ 📂 AI Development Tools
┃ ┣ 📂 Claude Code
┃ ┣ 📂 Cursor
┃ ┣ 📂 Windsurf
┃ ┣ 📂 Gemini CLI
┃ ┣ 📂 Codex
┃ ┣ 📂 Replit
┃ ┣ 📂 Bolt
┃ ┣ 📂 Lovable
┃ ┗ 📂 Vibe Coding
┃
┣ 📂 Production AI Systems
┃ ┣ 📂 Authentication
┃ ┣ 📂 Rate Limiting
┃ ┣ 📂 Cost Control
┃ ┣ 📂 Caching
┃ ┣ 📂 Queues
┃ ┣ 📂 Background Jobs
┃ ┣ 📂 Scaling
┃ ┣ 📂 Reliability
┃ ┗ 📂 Incident Response
┃
┗ 📂 AI Career Growth
┣ 📂 Portfolio Projects
┣ 📂 Open Source Contributions
┣ 📂 Technical Writing
┣ 📂 Interview Preparation
┣ 📂 AI Engineering Roadmaps
┣ 📂 Freelancing
┣ 📂 Startup Building
┣ 📂 Consulting
┗ 📂 AI Leadership

[Raytar @Raytar]

My friend makes $1.2 million a year as an Anthropic engineer.

I asked him how he learned prompting so well.

He sent me a video that was never supposed to get out. Their core team's prompting playbook.

You won’t find anything better about prompting than this video.

I watched it last night.

Halfway through, I realized I've been using Claude completely wrong for two years.

Watch it, then read the article below.

[Movez @0xMovez]

Andrew Ng:

"100% of my tasks are now done by AI agents - hype has exceeded my expectations. Loops is next step.

in 3-6 months, everyone will be using self-improving loops. No more prompting."

In a 30-minute talk, Andrew Ng explains how to build self-improving agentic systems from scratch.

Worth more than a $500 agentic course.

[darkzodchi @zodchiii]

Google CEO, Sundar Pichai:

"If you don't learn to how to orchestrate agents now, you'll spend 2027 catching up to people who started today"

In 30 minutes he explains why the best engineers stopped writing code and started running agents.

Watch the interview, then save the exact setup below 👇

[Fernando @Franc0Fernand0]

If you want to get better at algorithms and data structures, read these 12 articles: ↓

[Fernando @Franc0Fernand0]

If you want to seriously level up your DSA, watch this lecture series by @pmavrin.

It has ~90 hours of content on all the most important competitive programming topics, explained in a very accessible way. I loved his teaching.

Link below:

[the anonymous @dev_thrvikrman]

@Ayussheth Like to join 😉

[Rahul @sairahul1]

Anthropic pays $750,000+ a year for engineers who can build LLMs from scratch.

Not how to prompt them.
Not how to fine-tune them.
Not how to build RAG pipelines.

But how to build them from scratch.

This 2-hour Stanford lecture teaches you everything.

Scaling laws.
Data collection.
Architecture design.
Post-training alignment.

Free. From Stanford.

Watch first. Then read this.

The lecture is the theory.

And this article shows you how to actually build it (with code) ↓

[Prajwal @0xPrajwal_]

We were last to witness this:
Pre GPT hackathons

[Asmit @coolcoder56]

DSA @neetcode1
System Design @bytebytego
Backend @hnasr
Frontend @FrontendMasters

only resources one needs.

Rest is practice, practice, and practice

[Hasan @Ubermenscchh]

🚨Anthropic recently showed a 24-minute workshop on how to actually do prompts for Claude.

Taught by the people who built it.

Free. No registration. No paywall.

I've seen $300 courses that don't cover what they teach in the first 8 minutes.

Watch it and bookmark it now.

[Priyansh Agarwal @Priyansh_31Dec]

Another really useful lecture on Greedy algorithms that you can check out, if you're into competitive programming and care about solving really difficult greedy problems from first principles

[Movez @0xMovez]

Google Cloud AI engineer just showed how they go from idea to deployed app at Google in 30-minutes using Claude.

26-minutes. free. by Google AI team.

one person + Claude + Google Cloud = a full engineering org running on a laptop.

worth more than any $500 vibe-coding course.